Title :
An Improved Genetic Algorithm for Flexible Job Shop Scheduling Problem
Author :
Jiang Liangxiao ; Du Zhongjun
Author_Institution :
Dept. of Comput. Sci., Sichuan Univ., Chengdu, China
Abstract :
Based on the analysis of the characteristics of Flexible Job-shop Scheduling Problem (FJSP), an improved genetic algorithm is proposed to minimize the make span. The algorithm adopts a new initialization method to improve the quality of the initial population and to accelerate the speed of the algorithm´s convergence. Considering the characteristic of the problem, reasonable chromosome encoding, crossover and mutation operator are given, and then the effectiveness of the improved algorithm is proved by testing.
Keywords :
genetic algorithms; job shop scheduling; FJSP; algorithm convergence; chromosome encoding; crossover operator; flexible job shop scheduling problem; improved genetic algorithm; initialization method; makespan minimization; mutation operator; Biological cells; Encoding; Genetic algorithms; Job shop scheduling; Sociology; Statistics; Flexible Job Shop Scheduling; Genetic algorithm; Initialization population;
Conference_Titel :
Information Science and Control Engineering (ICISCE), 2015 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4673-6849-0
DOI :
10.1109/ICISCE.2015.36
